Triple
T18724494
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Language Models are Few-Shot Learners |
E457860
|
entity |
| Predicate | modelParameterCount |
P20681
|
FINISHED |
| Object | 175 billion |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: 175 billion | Statement: [Language Models are Few-Shot Learners, modelParameterCount, 175 billion]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: modelParameterCount Context triple: [Language Models are Few-Shot Learners, modelParameterCount, 175 billion]
-
A.
parameterCount
Indicates the number of parameters associated with a given function, method, or callable entity.
-
B.
numberOfModels
Indicates the quantity or count of models associated with a given entity or context.
-
C.
numberOfParametersOfLargestVariant
chosen
Indicates the total count of parameters in the variant that has the greatest number of parameters among all variants of an entity.
-
D.
parameterCountRelativeTo
Indicates a relationship comparing the number of parameters of one entity (such as a function, method, or operation) to that of another entity.
-
E.
numberOfConfigurations
Indicates the total count of distinct configurations associated with or applicable to a given entity or situation.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d8d393ba9c8190a8b03b04ddbb0a09 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e56d72d2c4819080b0d31860976b5e |
completed | April 20, 2026, 12:04 a.m. |
| PD | Predicate disambiguation | batch_69e48d03766c8190a43f7681842f4f8d |
completed | April 19, 2026, 8:06 a.m. |
Created at: April 10, 2026, 11:50 a.m.